Abstract
Face spoofing is an attack attempt to obtain unauthorized access by using photos, videos, or 3D maps of a user’s face. The development of anti-spoofing strategies evolves at the same time as facial authentication technologies. Many methods for preventing such attacks have been proposed recently [1,2,3], showing excellent results accuracy in fraud detection. However, most of these methods are very efficient in detecting patterns—such as fraud—present a major disadvantage: a high computational cost. This cost directly impacts the user experience of the facial authentication system, since the spoofing verification adds an extra layer of inference by artificial intelligence models, causing a longer waiting time for the authentication system’s user. This impact is most noted when the inference is performed on devices with limited computational power, such as mobile, tablets, and edge devices. In this work, we carry out an experimental analysis of the common anti-spoofing strategies considering the trade-off between correctness fraud detection and computational cost, aimed at optimizing the user experience. We also propose to use a fine-tuned Convolutional Neural Network (CNN) with a base network trained on a larger dataset and adds to our analysis.
Supported by Sidia Institute of Science and Technology, and Samsung Eletrônica da Amazônia Ltda, under the auspice of the Brazilian informatics law no 8.387/91.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Khurshid, A., Tamayo, S.C., Fernandes, E., Gadelha, M.R., Teofilo, M.: A robust and real-time face anti-spoofing method based on texture feature analysis. In: Stephanidis, C. (ed.) HCII 2019. LNCS, vol. 11786, pp. 484–496. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30033-3_37
Sengur, A., Akhtar, Z., Akbulut, Y., Ekici, S., Budak, U.: Deep feature extraction for face liveness detection. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–4, September 2018
Kim, W., Suh, S., Han, J.: Face liveness detection from a single image via diffusion speed model. IEEE Trans. Image Process. 24(8), 2456–2465 (2015)
Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2016)
Khurshid, A., Scharcanski, J.: Incremental multi-model dictionary learning for face tracking. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6, May 2018
Omidyeganeh, M., et al.: Yawning detection using embedded smart cameras. IEEE Trans. Instrum. Meas. 65(3), 570–582 (2016)
Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 504–517. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_37
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1–7. IEEE (2012)
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of Fourier spectra. In: Biometric Technology for Human Identification, vol. 5404, pp. 296–304. International Society for Optics and Photonics (2004)
Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: International Joint Conference on Biometrics, pp. 1–7. IEEE (2011)
Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 252–260. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_27
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. CoRR abs/1408.5601 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732, June 2014
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1519–1524 (2006)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Khurshid, A., Grunitzki, R. (2021). An Experimental Analysis of Face Anti-spoofing Strategies for Real Time Applications. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-90179-0_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90178-3
Online ISBN: 978-3-030-90179-0
eBook Packages: Computer ScienceComputer Science (R0)